Ship recourse optimization

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.

What is claimed is: 
     
         1 . A method comprising:
 obtaining shipping network data from a shipping network;   providing the shipping network data to one or more machine learning models configured to predict future shipping network data under two or more environmental conditions; and   determining, using output of the one or more machine learning models, one or more actions to perform in the shipping network.   
     
     
         2 . The method of  claim 1 , comprising:
 generating an agent-based model of the shipping network, wherein each ship of the shipping network is represented as an agent in the agent-based model, and wherein the agent-based model includes dynamic modeling of environmental conditions.   
     
     
         3 . The method of  claim 1 , wherein determining the one or more actions to perform in the shipping network comprises:
 performing mixed-integer programming using the output of the one or more machine learning models.   
     
     
         4 . The method of  claim 1 , comprising:
 determining the one or more actions to perform in the shipping network using (1) heuristics, (2) mixed-integer programming, and (3) an agent-based model of the shipping network.   
     
     
         5 . The method of  claim 1 , obtaining the shipping network data in response to detecting a condition within the shipping network. 
     
     
         6 . The method of  claim 5 , wherein the condition within the shipping network represents a ship of the shipping network being delayed. 
     
     
         7 . The method of  claim 1 , comprising:
 generating one or more candidate modifications to the shipping network.   
     
     
         8 . The method of  claim 7 , wherein the one or more candidate modifications include modifying one or more routes for ships of the shipping network. 
     
     
         9 . The method of  claim 8 , comprising:
 identifying routes of a set of the ships of the shipping network to modify using heuristics.   
     
     
         10 . The method of  claim 9 , comprising:
 determining modifications of the routes of the set of the ships of the shipping network using mixed-integer programming and an agent-based model of the shipping network.   
     
     
         11 . The method of  claim 8 , comprising:
 obtaining heuristic data indicating a modification for a first ship of the ships of the shipping network; and   wherein generating the one or more candidate modifications to the shipping network comprises using the heuristic data indicating the modification for the first ship of the ships of the shipping network.   
     
     
         12 . The method of  claim 1 , wherein determining the one or more actions to perform in the shipping network comprises:
 providing a user interface to a user device; and   obtaining user input indicating the one or more actions to perform in the shipping network.   
     
     
         13 . The method of  claim 1 , comprising:
 performing the one or more actions in the shipping network.   
     
     
         14 . The method of  claim 13 , wherein performing the one or more actions in the shipping network comprises:
 transmitting a signal to one or more computing elements controlling a ship in the shipping network.   
     
     
         15 . The method of  claim 14 , wherein the signal is configured to reroute the ship. 
     
     
         16 . A non-transitory computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
 obtaining shipping network data from a shipping network;   providing the shipping network data to one or more machine learning models configured to predict future shipping network data under two or more environmental conditions; and   determining, using output of the one or more machine learning models, one or more actions to perform in the shipping network.   
     
     
         17 . The medium of  claim 16 , the operations comprising:
 generating an agent-based model of the shipping network, wherein each ship of the shipping network is represented as an agent in the agent-based model, and wherein the agent-based model includes dynamic modeling of environmental conditions.   
     
     
         18 . The medium of  claim 16 , wherein determining the one or more actions to perform in the shipping network comprises:
 performing mixed-integer programming using the output of the one or more machine learning models.   
     
     
         19 . The medium of  claim 16 , the operations comprising:
 determining the one or more actions to perform in the shipping network using (1) heuristics, (2) mixed-integer programming, and (3) an agent-based model of the shipping network.   
     
     
         20 . A system, comprising:
 one or more processors; and   machine-readable media interoperably coupled with the one or more processors and storing one or more instructions that, when executed by the one or more processors, perform operations comprising:   obtaining shipping network data from a shipping network;   providing the shipping network data to one or more machine learning models configured to predict future shipping network data under two or more environmental conditions; and   determining, using output of the one or more machine learning models, one or more actions to perform in the shipping network.